Showing papers on "Metabolome published in 2013"
TL;DR: A comprehensive, quantitative, metabolome-wide characterization of human urine and the identification and annotation of several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage are undertaken.
Abstract: Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca.
1,118 citations
TL;DR: The fact that single-cell measurements exhibited a much larger spread in metabolite concentrations than population measurements was exploited to determine many metabolite-metabolite correlations, which were altered in 2DG-treated yeast cells relative to controls.
Abstract: Background In recent years, there has been a surge in the development and application of single-cell genomics, transcriptomics, proteomics, and metabolomics. The metabolome is defined as the full complement of small-molecule metabolites found in a specific cell, organ, or organism. The most interesting potential application of single-cell metabolomics may be in the area of cancer—for example, identification of circulating cancer cells that lead to metastasis. Other fields where single-cell metabolomics is expected to have an impact are systems biology, stem cell research, aging, and the development of drug resistance; more generally, it could be used to discover cells’ chemical strategies for coping with chemical or environmental stress. Relative to other single-cell “-omics” measurements, metabolomics provides a more immediate and dynamic picture of the functionality (i.e., of the phenotype) of a cell, but is arguably also the most difficult to measure. This is because the metabolome can dynamically react to the environment on a very short time scale (seconds or less), because of the large structural diversity and huge dynamic range of metabolites, because it is not possible to amplify metabolites, and because tagging them with fluorescent labels would distort their normal function. Single-cell analysis uses a wide variety of imaging and chemical analysis methods to study vastly different cell types and sizes. (A) Closterium acerosum (algal cells, ~300 μm × 40 μm; optical micrograph). (B) Euglena gracilis (algal cells, diameter ~20 μm); Raman image of β-carotene distribution (left) and fluorescence emission from proplastids (right). (C) Baker’s yeast (diameter ~5 μm); optical micrograph. (D) Escherichia coli (diameter ~0.75 μm, length 1 to 3 μm); fluorescence micrograph (image courtesy of M. Heinemann, University of Groningen). Advances Although deep biological insight based on single-cell metabolomics has not yet been obtained, important steps have been taken toward this goal. Advances in mass spectrometry (MS), MS imaging, capillary electrophoresis, optical spectroscopy, and in the development of fluorescence biosensors now allow the simultaneous determination of hundreds of metabolites in a single cell, with sensitivities in the attomole range. Modern array formats, in particular microfluidic platforms, contribute to our ability to perform such measurements rapidly and with high throughput. Several recent studies show how novel biological insight can be extracted from single-cell metabolomics. Substantial differences in the metabolomes of different snail neurons—for example, in B1 and B2 type neurons—have been found, immediately after isolating them and after overnight culturing. Glycosphingolipids could be labeled with a fluorescent tag, and in lysates of neurons incubated with such conjugates, all metabolic products derived from them were fluorescent and could be identified. Phosphorylation of 3′-deoxy-3′-fluorothymidine in lymphoma cells and solid tumors could be followed after treatment with cancer drugs. The biological effect of treating yeast cells by 2-deoxy-d-glucose (2DG) on the metabolome could be followed. The fact that single-cell measurements exhibited a much larger spread in metabolite concentrations than population measurements was exploited to determine many metabolite-metabolite correlations, which were altered in 2DG-treated yeast cells relative to controls. Outlook The metabolome is an excellent indicator of phenotypic heterogeneity and has been recognized as a key factor in rare-cell survival when populations are subjected to major chemical or environmental challenges. Metabolomics at the single-cell level, however, is only just coming of age. Improvements leading to more complete coverage of the metabolome, better and faster identification of metabolites, and nondestructive measurement are anticipated.
519 citations
TL;DR: It is suggested that 2-AAA is a marker of diabetes risk and a potential modulator of glucose homeostasis in humans and is not well correlated with other metabolite biomarkers of diabetes, such as branched chain amino acids and aromatic amino acids.
Abstract: Improvements in metabolite-profiling techniques are providing increased breadth of coverage of the human metabolome and may highlight biomarkers and pathways in common diseases such as diabetes. Using a metabolomics platform that analyzes intermediary organic acids, purines, pyrimidines, and other compounds, we performed a nested case-control study of 188 individuals who developed diabetes and 188 propensity-matched controls from 2,422 normoglycemic participants followed for 12 years in the Framingham Heart Study. The metabolite 2-aminoadipic acid (2-AAA) was most strongly associated with the risk of developing diabetes. Individuals with 2-AAA concentrations in the top quartile had greater than a 4-fold risk of developing diabetes. Levels of 2-AAA were not well correlated with other metabolite biomarkers of diabetes, such as branched chain amino acids and aromatic amino acids, suggesting they report on a distinct pathophysiological pathway. In experimental studies, administration of 2-AAA lowered fasting plasma glucose levels in mice fed both standard chow and high-fat diets. Further, 2-AAA treatment enhanced insulin secretion from a pancreatic β cell line as well as murine and human islets. These data highlight a metabolite not previously associated with diabetes risk that is increased up to 12 years before the onset of overt disease. Our findings suggest that 2-AAA is a marker of diabetes risk and a potential modulator of glucose homeostasis in humans.
360 citations
TL;DR: External validation of cirrhosis and hepatocellular carcinoma serum specimens showed that this combination biomarker, betaine and propionylcarnitine, is useful for diagnosis of liver cancer with a supplementary role to α-fetoprotein.
Abstract: Hepatocellular carcinoma has a poor prognosis due to its rapid development and early metastasis. In this report, we characterized the metabolic features of hepatocellular carcinoma using a nontargeted metabolic profiling strategy based on liquid chromatography-mass spectrometry. Fifty pairs of liver cancer samples and matched normal tissues were collected from patients having hepatocellular carcinoma, including tumor tissues, adjacent noncancerous tissues, and distal noncancerous tissues, and 105 metabolites were filtered and identified from the tissue metabolome. The principal metabolic alternations in HCC tumors included elevated glycolysis, gluconeogenesis, and β-oxidation with reduced tricarboxylic acid cycle and Δ-12 desaturase. Furthermore, increased levels of glutathione and other antioxidative molecules, together with decreased levels of inflammatory-related polyunsaturated fatty acids and phospholipase A2, were observed. Differential metabolite levels in tissues were tested in 298 serum specimens from patients with chronic hepatitis, cirrhosis, and hepatocellular carcinoma. Betaine and propionylcarnitine were confirmed to confer good diagnostic potential to distinguish hepatocellular carcinoma from chronic hepatitis and cirrhosis. External validation of cirrhosis and hepatocellular carcinoma serum specimens further showed that this combination biomarker is useful for diagnosis of hepatocellular carcinoma with a supplementary role to α-fetoprotein.
331 citations
TL;DR: It is shown that simplified communities can drive major changes in the host metabolomic profile, and is demonstrated that metabolomics constitutes a powerful avenue for functional characterization of the intestinal microbiota and its interaction with the host.
Abstract: Defining the functional status of host-associated microbial ecosystems has proven challenging owing to the vast number of predicted genes within the microbiome and relatively poor understanding of community dynamics and community–host interaction. Metabolomic approaches, in which a large number of small molecule metabolites can be defined in a biological sample, offer a promising avenue to ‘fingerprint' microbiota functional status. Here, we examined the effects of the human gut microbiota on the fecal and urinary metabolome of a humanized (HUM) mouse using an optimized ultra performance liquid chromatography–mass spectrometry-based method. Differences between HUM and conventional mouse urine and fecal metabolomic profiles support host-specific aspects of the microbiota's metabolomic contribution, consistent with distinct microbial compositions. Comparison of microbiota composition and metabolome of mice humanized with different human donors revealed that the vast majority of metabolomic features observed in donor samples are produced in the corresponding HUM mice, and individual-specific features suggest ‘personalized' aspects of functionality can be reconstituted in mice. Feeding the mice a defined, custom diet resulted in modification of the metabolite signatures, illustrating that host diet provides an avenue for altering gut microbiota functionality, which in turn can be monitored via metabolomics. Using a defined model microbiota consisting of one or two species, we show that simplified communities can drive major changes in the host metabolomic profile. Our results demonstrate that metabolomics constitutes a powerful avenue for functional characterization of the intestinal microbiota and its interaction with the host.
278 citations
TL;DR: A genome-wide association study of 217 plasma metabolites, including >100 not measured in prior GWAS, in 2076 participants of the Framingham Heart Study finds that estimated heritability explains >20% of interindividual variation, and that variation attributable to heritable factors is greater than that attributable to clinical factors.
259 citations
TL;DR: The metabolome data and the approach provided here can serve as a blueprint for the analysis of traits and conditions linking crop yield and senescence as well as provide clues to source-sink relations.
Abstract: Developmental senescence is a coordinated physiological process in plants and is critical for nutrient redistribution from senescing leaves to newly formed sink organs, including young leaves and developing seeds. Progress has been made concerning the genes involved and the regulatory networks controlling senescence. The resulting complex metabolome changes during senescence have not been investigated in detail yet. Therefore, we conducted a comprehensive profiling of metabolites, including pigments, lipids, sugars, amino acids, organic acids, nutrient ions, and secondary metabolites, and determined approximately 260 metabolites at distinct stages in leaves and siliques during senescence in Arabidopsis (Arabidopsis thaliana). This provided an extensive catalog of metabolites and their spatiotemporal cobehavior with progressing senescence. Comparison with silique data provides clues to source-sink relations. Furthermore, we analyzed the metabolite distribution within single leaves along the basipetal sink-source transition trajectory during senescence. Ceramides, lysolipids, aromatic amino acids, branched chain amino acids, and stress-induced amino acids accumulated, and an imbalance of asparagine/aspartate, glutamate/glutamine, and nutrient ions in the tip region of leaves was detected. Furthermore, the spatiotemporal distribution of tricarboxylic acid cycle intermediates was already changed in the presenescent leaves, and glucosinolates, raffinose, and galactinol accumulated in the base region of leaves with preceding senescence. These results are discussed in the context of current models of the metabolic shifts occurring during developmental and environmentally induced senescence. As senescence processes are correlated to crop yield, the metabolome data and the approach provided here can serve as a blueprint for the analysis of traits and conditions linking crop yield and senescence.
254 citations
TL;DR: The results suggest that microbes are syntropic with mucosal metabolome composition and therefore may be the source of and/or dependent upon gut epithelial metabolites.
Abstract: Background: Consistent compositional shifts in the gut microbiota are observed in IBD and other chronic intestinal disorders and may contribute to pathogenesis. The identities of microbial biomolecular mechanisms and metabolic products responsible for disease phenotypes remain to be determined, as do the means by which such microbial functions may be therapeutically modified. Results: The composition of the microbiota and metabolites in gut microbiome samples in 47 subjects were determined. Samples were obtained by endoscopic mucosal lavage from the cecum and sigmoid colon regions, and each sample was sequenced using the 16S rRNA gene V4 region (Illumina-HiSeq 2000 platform) and assessed by UPLC mass spectroscopy. Spearman correlations were used to identify widespread, statistically significant microbial-metabolite relationships. Metagenomes for identified microbial OTUs were imputed using PICRUSt, and KEGG metabolic pathway modules for imputed genes were assigned using HUMAnN. The resulting metabolic pathway abundances were mostly concordant with metabolite data. Analysis of the metabolome-driven distribution of OTU phylogeny and function revealed clusters of clades that were both metabolically and metagenomically similar. Conclusions: The results suggest that microbes are syntropic with mucosal metabolome composition and therefore may be the source of and/or dependent upon gut epithelial metabolites. The consistent relationship between inferred metagenomic function and assayed metabolites suggests that metagenomic composition is predictive to a reasonable degree of microbial community metabolite pools. The finding that certain metabolites strongly correlate with microbial community structure raises the possibility of targeting metabolites for monitoring and/or therapeutically manipulating microbial community function in IBD and other chronic diseases.
245 citations
TL;DR: When time and sample are limited, the maximum amount of biological information related to lipid and central carbon metabolism can be acquired by combining RPLC ESI positive and HILIC ESI negative mode analysis.
Abstract: Although the objective of any ‘omic science is broad measurement of its constituents, such coverage has been challenging in metabolomics because the metabolome is comprised of a chemically diverse set of small molecules with variable physical properties. While extensive studies have been performed to identify metabolite isolation and separation methods, these strategies introduce bias toward lipophilic or water-soluble metabolites depending on whether reversed-phase (RP) or hydrophilic interaction liquid chromatography (HILIC) is used, respectively. Here we extend our consideration of metabolome isolation and separation procedures to integrate RPLC/MS and HILIC/MS profiling. An aminopropyl-based HILIC/MS method was optimized on the basis of mobile-phase additives and pH, followed by evaluation of reproducibility. When applied to the untargeted study of perturbed bacterial metabolomes, the HILIC method enabled the accurate assessment of key, dysregulated metabolites in central carbon pathways (e.g., amino ...
244 citations
TL;DR: Soil microbiome-driven changes in leaf metabolomics revealed that leaf amino acid content was positively correlated with both microbiome composition and insect feeding behavior and revealed that most microbiome-treated plants showed inhibition to larvae feeding, compared with unamended control plants.
Abstract: Summary
It is known that environmental factors can affect the biosynthesis of leaf metabolites. Similarly, specific pairwise plant–microbe interactions modulate the plant's metabolome by stimulating production of phytoalexins and other defense-related compounds. However, there is no information about how different soil microbiomes could affect the plant growth and the leaf metabolome.
We analyzed experimentally how diverse soil microbiomes applied to the roots of Arabidopsis thaliana were able to modulate plant growth and the leaf metabolome, as assessed by GC-MS analyses. Further, we determined the effects of soil microbiome-driven changes in leaf metabolomics on the feeding behavior of Trichopulsia ni larvae.
Soil microbiomes differentially impacted plant growth patterns as well as leaf metabolome composition. Similarly, most microbiome-treated plants showed inhibition to larvae feeding, compared with unamended control plants. Pyrosequencing analysis was conducted to determine the soil microbial composition and diversity of the soils used in this study.
Correlation analyses were performed to determine relationships between various factors (soil microbial taxa, leaf chemical components, plant growth patterns and insect feeding behavior) and revealed that leaf amino acid content was positively correlated with both microbiome composition and insect feeding behavior.
212 citations
TL;DR: The variability in certain milk metabolites suggests possible roles in infant or infant gut microbial development and exploration of the interrelations between the milk sugars revealed significant positive and negative associations.
Abstract: Breast milk delivers nutrition and protection to the developing infant. There has been considerable research on the high-molecular-weight milk components; however, low-molecular-weight metabolites have received less attention. To determine the effect of maternal phenotype and diet on the human milk metabolome, milk collected at day 90 postpartum from 52 healthy women was analyzed by using proton nuclear magnetic resonance spectroscopy. Sixty-five milk metabolites were quantified (mono-, di-, and oligosaccharides; amino acids and derivatives; energy metabolites; fatty acids and associated metabolites; vitamins, nucleotides, and derivatives; and others). The biological variation, represented as the percentage CV of each metabolite, varied widely (4-120%), with several metabolites having low variation (<20%), including lactose, urea, glutamate, myo-inositol, and creatinine. Principal components analysis identified 2 clear groups of participants who were differentiable on the basis of milk oligosaccharide concentration and who were classified as secretors or nonsecretors of fucosyltransferase 2 (FUT2) gene products according to the concentration of 2'-fucosyllactose, lactodifucotetraose, and lacto-N-fucopentaose I. Exploration of the interrelations between the milk sugars by using Spearman rank correlations revealed significant positive and negative associations, including positive correlations between fucose and products of the FUT2 gene and negative correlations between fucose and products of the fucosyltransferase 3 (FUT3) gene. The total concentration of milk oligosaccharides was conserved among participants (%CV = 18%), suggesting tight regulation of total oligosaccharide production; however, concentrations of specific oligosaccharides varied widely between participants (%CV = 30.4-84.3%). The variability in certain milk metabolites suggests possible roles in infant or infant gut microbial development. This trial was registered at clinicaltrials.gov as NCT01817127.
TL;DR: This work has developed a web-based metabolite identification tool that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8,021 known human endogenous metabolites and their predicted metabolic products and it is shown that the evidence-based metabolites library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries.
Abstract: Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8 021 known human endogenous metabolites and their predicted metabolic products (375 809 compounds from one metabolic reaction and 10 583 901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (...
TL;DR: It is shown that cerebral metabolites are influenced by normal intestinal microbiota through the microbiota-gut-brain axis, and indicates that normal intestine microbiota closely connected with brain health and disease, development, attenuation, learning, memory, and behavior.
Abstract: Recent studies suggest that intestinal microbiota influences gut-brain communication. In this study, we aimed to clarify the influence of intestinal microbiota on cerebral metabolism. We analyzed the cerebral metabolome of germ-free (GF) mice and ex-germ-free (Ex-GF) mice, which were inoculated with suspension of feces obtained from specific pathogen free mice, using capillary electrophoresis with time-of-flight mass spectrometry (CE-TOFMS). CE-TOFMS identified 196 metabolites from the cerebral metabolome in both GF and Ex-GF mice. The concentrations of 38 metabolites differed significantly (p < 0.05) between GF and Ex-GF mice. Approximately 10 of these metabolites are known to be involved in brain function, whilst the functions of the remainder are unclear. Furthermore, we observed a novel association between cerebral glycolytic metabolism and intestinal microbiota. Our work shows that cerebral metabolites are influenced by normal intestinal microbiota through the microbiota-gut-brain axis, and indicates that normal intestinal microbiota closely connected with brain health and disease, development, attenuation, learning, memory, and behavior.
TL;DR: A novel approach to simultaneously perform targeted and non-targeted metabolomics as well as lipidomics from one small piece of liver or muscle tissue by ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS) following a methyl tert-butyl ether (MTBE)-based extraction.
TL;DR: A targeted, quantitative assay of the NAD+ metabolome with the use of HPLC coupled to mass spectrometry is developed, which incorporates new separations and improves upon a previously published method that suffered from the problem of ionization suppression for particular compounds.
Abstract: Nicotinamide adenine dinucleotide (NAD+) is a coenzyme for hydride transfer reactions and a substrate for sirtuins and other NAD+-consuming enzymes. The abundance of NAD+, NAD+ biosynthetic intermediates, and related nucleotides reflects the metabolic state of cells and tissues. High performance liquid chromatography (HPLC) followed by ultraviolet-visible (UV-Vis) spectroscopic analysis of NAD+ metabolites does not offer the specificity and sensitivity necessary for robust quantification of complex samples. Thus, we developed a targeted, quantitative assay of the NAD+ metabolome with the use of HPLC coupled to mass spectrometry. Here we discuss NAD+ metabolism as well as the technical challenges required for reliable quantification of the NAD+ metabolites. The new method incorporates new separations and improves upon a previously published method that suffered from the problem of ionization suppression for particular compounds.
TL;DR: A genetic analysis of the rice metabolome resulted in the identification of hundreds of metabolic quantitative trait loci with both high resolution and large effects, which may help bridge the gap between the genome and phenome.
Abstract: Plant metabolites are crucial for both plant life and human nutrition. Despite recent advance in metabolomics, the genetic control of plant metabolome remains largely unknown. Here, we performed a genetic analysis of the rice metabolome that provided over 2,800 highly resolved metabolic quantitative trait loci for 900 metabolites. Distinct and overlapping accumulation patterns of metabolites were observed and complex genetic regulation of metabolism was revealed in two different tissues. We associated 24 candidate genes to various metabolic quantitative trait loci by data mining, including ones regulating important morphological traits and biological processes. The corresponding pathways were reconstructed by updating in vivo functions of previously identified and newly assigned genes. This study demonstrated a powerful tool and provided a vast amount of high-quality data for understanding the plasticity of plant metabolome, which may help bridge the gap between the genome and phenome.
TL;DR: The development of an ultra-high-pressure liquid chromatography time-of-flight mass spectrometry (UHPLC-TOF-MS) based metabolomics approach illustrates that herbivore attack leads to the induction of metabolites that can have contrasting effects on Herbivore resistance in the leaves and roots.
Abstract: Plants respond to herbivory by reprogramming their metabolism. Most research in this context has focused on locally induced compounds that function as toxins or feeding deterrents. We developed an ultra-high-pressure liquid chromatography time-of-flight mass spectrometry (UHPLC-TOF-MS)-based metabolomics approach to evaluate local and systemic herbivore-induced changes in maize leaves, sap, roots and root exudates without any prior assumptions about their function. Thirty-two differentially regulated compounds were identified from Spodoptera littoralis-infested maize seedlings and isolated for structure assignment by microflow nuclear magnetic resonance (CapNMR). Nine compounds were quantified by a high throughput direct nano-infusion tandem mass spectrometry/mass spectrometry (MS/MS) method. Leaf infestation led to a marked local increase of 1,3-benzoxazin-4-ones, phospholipids, N-hydroxycinnamoyltyramines, azealic acid and tryptophan. Only few changes were found in the root metabolome, but 1,3-benzoxazin-4-ones increased in the vascular sap and root exudates. The role of N-hydroxycinnamoyltyramines in plant–herbivore interactions is unknown, and we therefore tested the effect of the dominating p-coumaroyltyramine on S. littoralis. Unexpectedly, p-coumaroyltyramine was metabolized by the larvae and increased larval growth, possibly by providing additional nitrogen to the insect. Taken together, this study illustrates that herbivore attack leads to the induction of metabolites that can have contrasting effects on herbivore resistance in the leaves and roots.
TL;DR: In this article, a system level characterization of serum metabolome and lipidome of adult Dengue fever/dengue hemorrhagic fever (DF/DHF) patients at early febrile, defervescence, and convalescent stages of infection was performed using liquid chromatography and gas chromatography-mass spectrometry.
Abstract: Background: Dengue virus (DENV) is the most widespread arbovirus with an estimated 100 million infections occurring every year. Endemic in the tropical and subtropical areas of the world, dengue fever/dengue hemorrhagic fever (DF/DHF) is emerging as a major public health concern. The complex array of concurrent host physiologic changes has hampered a complete understanding of underlying molecular mechanisms of dengue pathogenesis. Methodology/Principle Findings: Systems level characterization of serum metabolome and lipidome of adult DF patients at early febrile, defervescence, and convalescent stages of DENV infection was performed using liquid chromatography- and gas chromatography-mass spectrometry. The tractability of following metabolite and lipid changes in a relatively large sample size (n=44) across three prominent infection stages allowed the identification of critical physiologic changes that coincided with the different stages. Sixty differential metabolites were identified in our metabolomics analysis and the main metabolite classes were free fatty acids, acylcarnitines, phospholipids, and amino acids. Major perturbed metabolic pathways included fatty acid biosynthesis and b-oxidation, phospholipid catabolism, steroid hormone pathway, etc., suggesting the multifactorial nature of human host responses. Analysis of phospholipids and sphingolipids verified the temporal trends and revealed association with lymphocytes and platelets numbers. These metabolites were significantly perturbed during the early stages, and normalized to control levels at convalescent stage, suggesting their potential utility as prognostic markers. Conclusions/Significance: DENV infection causes temporally distinct serum metabolome and lipidome changes, and many of the differential metabolites are involved in acute inflammatory responses. Our global analyses revealed early antiinflammatory responses working in concert to modulate early pro-inflammatory processes, thus preventing the host from development of pathologies by excessive or prolonged inflammation. This study is the first example of how an omicapproach can divulge the extensive, concurrent, and dynamic host responses elicited by DENV and offers plausible physiological insights to why DF is self limiting.
TL;DR: Positive correlations suggest that the rate-limiting steps in biosynthesis of glucosinolates in the root are oxidative modifications of side chains, which could indicate non–cell-autonomous mechanisms responsible for their targeted localization.
Abstract: Metabolite composition offers a powerful tool for understanding gene function and regulatory processes. However, metabolomics studies on multicellular organisms have thus far been performed primarily on whole organisms, organs, or cell lines, losing information about individual cell types within a tissue. With the goal of profiling metabolite content in different cell populations within an organ, we used FACS to dissect GFP-marked cells from Arabidopsis roots for metabolomics analysis. Here, we present the metabolic profiles obtained from five GFP-tagged lines representing core cell types in the root. Fifty metabolites were putatively identified, with the most prominent groups being glucosinolates, phenylpropanoids, and dipeptides, the latter of which is not yet explored in roots. The mRNA expression of enzymes or regulators in the corresponding biosynthetic pathways was compared with the relative metabolite abundance. Positive correlations suggest that the rate-limiting steps in biosynthesis of glucosinolates in the root are oxidative modifications of side chains. The current study presents a work flow for metabolomics analyses of cell-type populations.
TL;DR: The results imply that the systematic evaluation of existing methods and the development of customized methods for each microorganism are critical for metabolome sample preparation to facilitate the reliable and accurate analysis of metabolome.
Abstract: Metabolome sampling is one of the most important factors that determine the quality of metabolomics data. The main steps in metabolite sample preparation include quenching and metabolite extraction. Quenching with 60% (v/v) cold methanol at −40 °C has been most commonly used for Saccharomyces cerevisiae, and this method was recently modified as “leakage-free cold methanol quenching” using pure methanol at −40 °C. Boiling ethanol (75%, v/v) and cold pure methanol are the most widely used extraction solvents for S. cerevisiae. In the present study, metabolome sampling protocols, including the above methods, were evaluated by analyzing 110 identified intracellular metabolites of S. cerevisiae using gas chromatography/time-of-flight mass spectrometry. According to our results, fast filtration followed by washing with an appropriate volume of water can minimize the metabolite loss due to cell leakage as well as the contamination by extracellular metabolites. For metabolite extraction, acetonitrile/water mixtur...
TL;DR: Significant progress has been made in the development and application of bioanalytical tools for single cell metabolomics based on mass spectrometry, microfluidics, and capillary separations.
TL;DR: Insight is provided into the molecular basis of the therapeutic applications and the chemopreventive activities of certain probiotic metabolites, with emphasis on the interaction between these metabolites and the molecular signaling cascades that are considered to be epigenetic targets in preventing colon cancer.
Abstract: Dietary interventions for preventing colon cancer have recently attracted increased attention from researchers and clinicians. The probiotics have emerged as potential therapeutic agents but are also regarded as healthy dietary supplements for nutrition and health applications. The probiotic metabolome may interfere with various cellular and molecular processes, including the onset and progression of colon cancer. Probiotic metabolites may lead to the modulation of diverse cellular signal transduction and metabolic pathways. The gut microbial metabolites (organic acids, bacteriocins, peptides, etc.) have been noted to interact with multiple key targets in various metabolic pathways that regulate cellular proliferation, differentiation, apoptosis, inflammation, angiogenesis, and metastasis. Progress in this field suggests that epigenetic alterations will be widely used in the near future to manage colon cancer. The present review provides insights into the molecular basis of the therapeutic applications and the chemopreventive activities of certain probiotic metabolites, with emphasis on the interaction between these metabolites and the molecular signaling cascades that are considered to be epigenetic targets in preventing colon cancer.
TL;DR: Cold stress adaptation of C. reinhardtii is studied to integrate proteome, metabolome, physiological and cell-morphological changes during a time-course from 0 to 120 h and revealed correlations between proteins and metabolites otherwise not detectable.
TL;DR: It is hypothesized that an animal model (Tupaia belangeri chinensis) of HCV would produce a unique characterization of metabolic phenotypes and the creation of new treatment paradigms targeting and activating these networks in their entirety, rather than single proteins, might be necessary for controlling and treating HCV efficiently.
TL;DR: A protocol for investigating and quantifying metabolites in individual isolated neurons using single-cell capillary electrophoresis coupled to electrospray ionization (ESI) time-of-flight (TOF) MS found that a subset of identified compounds was sufficient to reveal metabolic differences among freshly isolated neurons of different types and changes in the metabolite profiles of cultured neurons.
Abstract: Single-cell mass spectrometry (MS) empowers metabolomic investigations by decreasing analytical dimensions to the size of individual cells and subcellular structures. We describe a protocol for investigating and quantifying metabolites in individual isolated neurons using single-cell capillary electrophoresis (CE) coupled to electrospray ionization (ESI) time-of-flight (TOF) MS. The protocol requires ∼2 h for sample preparation, neuron isolation and metabolite extraction, and 1 h for metabolic measurement. We used the approach to detect more than 300 distinct compounds in the mass range of typical metabolites in various individual neurons (25-500 μm in diameter) isolated from the sea slug (Aplysia californica) central and rat (Rattus norvegicus) peripheral nervous systems. We found that a subset of identified compounds was sufficient to reveal metabolic differences among freshly isolated neurons of different types and changes in the metabolite profiles of cultured neurons. The protocol can be applied to the characterization of the metabolome in a variety of smaller cells and/or subcellular domains.
TL;DR: A combined liquid-liquid and solid phase extraction method for plasma extraction and Qualitative analysis and quantitative analysis of both internal standards (ISTDs) and endogenous metabolites demonstrate excellent reproducibility with CV's below 15% for the combined method compared to 30% using the methanol method.
TL;DR: A public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these datasets with transcriptomic data to create hypotheses concerning specialized metabolisms that generate the diversity in natural product chemistry are detailed.
TL;DR: This work has undertaken an effort to comprehensively characterize the bovine ruminal fluid metabolome by using NMR spectroscopy, inductively coupled plasma mass-spectroscopy (ICP-MS), gas chromatography-mass spectrometry (GC-MS, direct flow injection (DFI), and lipidomics with computer-aided literature mining to identify and quantify essentially all of the metabolites in bovinesRuminal fluid that can be routinely detected.
Abstract: The rumen is a unique organ that serves as the primary site for microbial fermentation of ingested plant material for domestic livestock such as cattle, sheep and goats. The chemical composition of ruminal fluid is thought to closely reflect the healthy/unhealthy interaction between rumen microflora and diet. Just as diet and feed quality is important for livestock production, rumen health is also critical to the growth and production of high quality milk and meat. Therefore a detailed understanding of the chemical composition of ruminal fluid and the influence of diet on its composition could help improve the efficiency and effectiveness of farming and veterinary practices. Consequently we have undertaken an effort to comprehensively characterize the bovine ruminal fluid metabolome. In doing so, we combined NMR spectroscopy, inductively coupled plasma mass-spectroscopy (ICP-MS), gas chromatography-mass spectrometry (GC-MS), direct flow injection (DFI) mass spectrometry and lipidomics with computer-aided literature mining to identify and quantify essentially all of the metabolites in bovine ruminal fluid that can be routinely detected (with today’s technology). The use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these techniques. Tables containing the set of 246 ruminal fluid metabolites or metabolite species, their concentrations, related literature reference and links to their known diet associations for the bovine rumen metabolome are freely available at http://www.rumendb.ca
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TL;DR: Growth was clearly reduced and transcription of a large number of genes involved in oxidative stress defense mechanisms was differentially increased and metabolites involved in the glutathione synthesis pathway were also affected but the effects of cadmium were found to be more pronounced at the transcript level than in the metabolome, suggesting that the former exhibits greater sensitivity toward Cadmium exposure.
TL;DR: Novel alterations in metabolomic profiles between various neurodegenerative dementias are revealed, which would contribute to etiological investigations and possibly provide an objective method for diagnosing dementia‐type by multiphase screening.
Abstract: Despite increasing global prevalence, the precise pathogenesis and terms for objective diagnosis of neurodegenerative dementias remain controversial, and comprehensive understanding of the disease remains lacking. Here, we conducted metabolomic analysis of serum and saliva obtained from patients with neurodegenerative dementias (n = 10), including Alzheimer's disease, frontotemporal lobe dementia, and Lewy body disease, as well as from age-matched healthy controls (n = 9). Using CE-TOF-MS, six metabolites in serum (β-alanine, creatinine, hydroxyproline, glutamine, iso-citrate, and cytidine) and two in saliva (arginine and tyrosine) were significantly different between dementias and controls. Using multivariate analysis, serum was confirmed as a more efficient biological fluid for diagnosis compared to saliva; additionally, 45 metabolites in total were identified as candidate markers that could discriminate at least one pair of diagnostic groups from the healthy control group. These metabolites possibly provide an objective method for diagnosing dementia-type by multiphase screening. Moreover, diagnostic-type-dependent differences were observed in several tricarboxylic acid cycle compounds detected in serum, indicating that some pathways in glucose metabolism may be altered in dementia patients. This pilot study revealed novel alterations in metabolomic profiles between various neurodegenerative dementias, which would contribute to etiological investigations.